m6Adecom: Analysis of m 6 A profile matrix based on graph regularized non-negative matrix factorization

• Published in: Methods (San Diego, Calif.) Calif.), 2022-01, Vol.203, p.322
• Main Authors: Liu, Rucong, Liu, Leibo, Zhou, Yuan
• Format: Article
• Language: English
• ISSN: 1095-9130
• DOI: 10.1016/j.ymeth.2022.01.007

Epitranscriptomic m A methylation is shown to mediate extensive regulations under the context of various RNA binding protein (RBP) readers. With m A methylation data has reached a sizable scale, the functional context-aware analysis of m A profiles is becoming more feasible and demanded. In this study, we employed graph regularized non-negative matrix factorization (GNMF) for m A profile analysis and comparison, where the RBP binding preference of m A sites were incorporated as the functional context-based graph constraint term. Compared to the baseline non-negative matrix factorization (NMF) method, this GNMF-based method could better capture the distinctions in multiple functional characteristics between different group of m A sites, including but not limited to the associated biological pathways and disease genes. We further established m6Adecom, an online tool that can be used for correlation and enrichment analysis of m A profiles using the matrix decomposition result from GNMF, and gene set enrichment analysis based on the high-score m A sites. m6Adecom is freely accessible at http://www.rnanut.net/m6adecom.